U.S. patent application number 12/522896 was filed with the patent office on 2010-04-15 for method and system for detecting and monitoring emissions.
Invention is credited to Terry Dan Butler, Dennis Scott Prince.
Application Number | 20100094565 12/522896 |
Document ID | / |
Family ID | 39635602 |
Filed Date | 2010-04-15 |
United States Patent
Application |
20100094565 |
Kind Code |
A1 |
Prince; Dennis Scott ; et
al. |
April 15, 2010 |
METHOD AND SYSTEM FOR DETECTING AND MONITORING EMISSIONS
Abstract
A method and system for detecting, quantifying or characterizing
emitting sources. According to an embodiment, an emission source is
located by monitoring an area with one or more sensors, determining
a plume, generating one or more candidates for the emission source,
and using the plume to derive one or more characteristics
associated with the emission source, and then locating the emission
source based on agreement or convergence of the one or more
characteristics.
Inventors: |
Prince; Dennis Scott;
(Edmonton, CA) ; Butler; Terry Dan; (Edmonton,
CA) |
Correspondence
Address: |
BENNETT JONES LLP;C/O MS ROSEANN CALDWELL
4500 BANKERS HALL EAST, 855 - 2ND STREET, SW
CALGARY
AB
T2P 4K7
CA
|
Family ID: |
39635602 |
Appl. No.: |
12/522896 |
Filed: |
January 16, 2008 |
PCT Filed: |
January 16, 2008 |
PCT NO: |
PCT/CA08/00080 |
371 Date: |
December 22, 2009 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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60885172 |
Jan 16, 2007 |
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Current U.S.
Class: |
702/22 ;
73/170.11; 73/23.2 |
Current CPC
Class: |
G01N 1/26 20130101; G08B
21/12 20130101 |
Class at
Publication: |
702/22 ; 73/23.2;
73/170.11 |
International
Class: |
G01N 33/00 20060101
G01N033/00; G01N 7/00 20060101 G01N007/00; G01P 5/00 20060101
G01P005/00; G06F 19/00 20060101 G06F019/00 |
Claims
1. A method of characterizing a source of an emitted material, said
method comprising the steps of: measuring concentrations of the
emitted material from at least two or more locations; measuring
changes in representative wind velocity over time; generating a
first dimensionless plume and a second dimensionless plume in space
based on said measured wind velocity changes and said measured
concentrations at said first and said second locations,
respectively; determining a first trajectory based on said measured
wind velocity changes and said measured concentrations for said
first location; determining a second trajectory based on said
measured wind velocity changes and said measured concentrations for
said second location; determining a first emission source candidate
at a first location along said first trajectory, and said first
emission source having one or more characteristics; determining a
second emission source candidate at a second location along said
second trajectory, and said second emission source having one or
more characteristics; converting said first dimensionless plume
into a dimensioned plume at said first location and determining a
size characteristic based on said dimensioned plume, wherein said
size characteristic comprises one of the characteristics associated
with said first emission source candidate; converting said second
dimensionless plume into a dimensioned plume at said second
location and determining a size characteristic associated with said
second source candidate based on the dimensioned plume, wherein
said size characteristic comprises one of the characteristics
associated with said second emission source candidate; and
determining the source of the emitted material based on substantial
agreement between the location and the characteristics of said
first and second emission source candidates.
2. The method as claimed in claim 1, wherein said characteristics
associated with said emission source candidates include one or more
of emission rate variability, horizontal emission location,
vertical emission location, emission exit momentum, emission
buoyancy, point emission source, area emission source, multiple
emission sources, plume concentration profile for emission.
3. The method as claimed in claim 1, wherein said at least two or
more locations comprise a sensor stationed at a first location and
then a second location.
4. The method as claimed in claim 1, wherein said representative
wind velocity comprises the velocity of wind flowing from the
emission source to the measurement locations.
5. The method as claimed in claim 1, wherein said step of
determining a size characteristic based on said dimensioned plume
comprises determining a flux within the dimensioned plume and
background flux level, and subtracting the background flux from the
flux within said dimensioned plume, and multiplying the flux
difference with an area measurement of said dimensioned plume.
6. The method as claimed in claim 2, wherein said step of
determining a size characteristic comprises determining the
emission rate variability, and said emission rate variability is
determined by comparing one or more concentrations measured within
said dimensioned plume to an average concentration measurement and
the emission rate variability comprising deviations from the
average concentration measurement.
7. A method of monitoring a emission source in a defined area, said
method comprising the steps of: generating an emission profile for
the defined area; periodically measuring for emissions in the
defined; comparing said emission measurements to said background
emission level; determining a deviation between said emission
measurements and said emission profile; and recording any
deviations exceeding a pre-defined threshold.
8. The method as claimed in claim 7, further including the step of
mapping said recorded deviations and said mapping providing one or
more characteristics associated with the emission source.
9. The method as claimed in claim 8, wherein said characteristics
include one or more of size, emission rate variability, horizontal
emission location, vertical emission location, emission exit
momentum, emission buoyancy, point emission source, area emission
source, multiple emission sources, plume concentration profile for
emission.
10. A method of characterizing a source of an emitted material,
said method comprising the steps of: measuring concentrations of
the emitted material from at least one location; measuring changes
in wind velocity over time; generating a dimensionless plume in
space based on said measured concentrations and said measured wind
velocity changes; determining a trajectory for said dimensionless
plume; determining one or more characteristics associated with said
dimensionless plume; determining one or more emission source
candidates at locations along said trajectory, wherein each of said
candidates includes one or more characteristics; converting said
dimensionless plume into a dimensioned plume at the location of
each of said candidates, and determining a characteristic
associated with each of said emission source candidates based on
the corresponding dimensioned plume; obtaining a characteristic
associated with the source of the emission; and determining the
source of the emitted material based on substantial agreement
between the location of one of said emission source candidates and
the characteristic of the emission source.
11. A computer readable medium configured to store computer
readable instructions executable by a computing device to cause the
device to implement the steps of the method of claim 1.
12. A computer readable medium configured to store computer
readable instructions executable by a computing device to cause the
device to implement the steps of the method of claim 7.
13. A computer readable medium configured to store computer
readable instructions executable by a computing device to cause the
device to implement the steps of the method of claim 10.
Description
FIELD OF THE INVENTION
[0001] The present invention relates to air monitoring, and in
particular to a method and system for detecting, quantifying or
characterizing emitting sources.
BACKGROUND OF THE INVENTION
[0002] The reduction of industrial contaminant emissions continues
to be important in decreasing anthropogenic environmental impact.
Daunting challenges are being encountered as the bar continues to
rise with respect to both the amount and nature of contaminants
that are considered acceptable. This is particularly evident with
respect to Green House Gas (GHG) emission reductions. While large
CO.sub.2 emissions are a present concern with respect to the green
house effect, unintended emissions of methane would also be
important given that methane produces a greater green house effect.
The contribution of methane emissions to the overall anthropogenic
environmental impact is not presently well understood, however
there are indications that it may be significant.
[0003] In the field of air quality monitoring, human sensory
perception is relied upon to detect chemical or particulate plumes.
Visible plumes may also include condensation plumes, wherein an
emitted contaminant being otherwise invisible, becomes visible
under atmospheric temperature and pressure conditions causing the
contaminant to condensate or crystallize. If a plume has
distinctive visual characteristics, such as a distinctive color or
opaqueness, the plume can be tracked visually back to its source.
However, human visual sensory perception cannot be relied upon in
low lighting conditions.
[0004] Not all emissions have discernable visual characteristics.
Whether an emission does not have discernable visual
characteristics under typical environmental conditions, whether an
emission has discernable visual characteristics however it is of
such low concentration that human sensory perception is incapable
of perceiving the difference between the plume and the ambient air,
or whether smoggy environmental conditions render the plume
indistinguishable therefrom; even extremely low concentrations of
certain airborne contaminants can have a deleterious impact on the
environment and/or affect living entities large and small.
[0005] Accordingly, there remains a need for improvements in the
art.
BRIEF SUMMARY OF THE INVENTION
[0006] The present invention is directed to a method and system for
detecting, quantifying and/or characterizing sources of an emission
of a material or compound.
[0007] In accordance with a broad aspect of the present invention,
there is provided a method of characterizing a source of an emitted
material, the method comprises the steps of: measuring
concentrations of the emitted material from at least two or more
locations; measuring changes in representative wind velocity over
time; generating a first dimensionless plume and a second
dimensionless plume in space based on the measured wind velocity
changes and the measured concentrations at the first and the second
locations, respectively; determining a first trajectory based on
the measured wind velocity changes and the measured concentrations
for the first location; determining a second trajectory based on
the measured wind velocity changes and the measured concentrations
for the second location; determining a first emission source
candidate at a first location along the first trajectory, and the
first emission source having one or more characteristics;
determining a second emission source candidate at a second location
along the second trajectory, and the second emission source having
one or more characteristics; converting the first dimensionless
plume into a dimensioned plume at the first location and
determining a size characteristic based on said dimensioned plume,
wherein the size characteristic comprises one of the
characteristics associated with the first emission source
candidate; converting the second dimensionless plume into a
dimensioned plume at the second location and determining a size
characteristic associated with the second source candidate based on
the dimensioned plume, wherein the size characteristic comprises
one of the characteristics associated with the second emission
source candidate; and determining the source of the emitted
material based on substantial agreement between the location and
the characteristics of the first and second emission source
candidates.
[0008] In accordance with another broad aspect of the present
invention, there is provided a method of characterizing a source of
an emitted material, the method comprises the steps of: measuring
concentrations of the emitted material from at least one location;
measuring changes in wind velocity over time; generating a
dimensionless plume in space based on the measured concentrations
and the measured wind velocity changes; determining a trajectory
for the dimensionless plume; determining one or more
characteristics associated with the dimensionless plume;
determining one or more emission source candidates at locations
along the trajectory, wherein each of the candidates includes one
or more characteristics; converting the dimensionless plume into a
dimensioned plume at the location of each of the candidates, and
determining a characteristic associated with each of the emission
source candidates based on the corresponding dimensioned plume;
obtaining a characteristic associated with the source of the
emission; and determining the source of the emitted material based
on substantial agreement between the location of one of the
emission source candidates and the characteristic of the emission
source.
[0009] According to another embodiment, there is provided a
computer readable medium configured to store computer readable
instructions executable by a computing device to cause the device
to implement one or more of the processes and method described
herein.
[0010] In accordance with a further aspect of the present
invention, there is provided a method of characterizing a source
emitting a contaminant into a moving fluid, the contaminant forming
a plume, the method comprising the steps of measuring a variation
of a diluted concentration of the contaminant perceived at one or
more sampling inlet locations about the source, the diluted
contaminant concentration varying over time with a corresponding
representative wind velocity; deriving a variation of a diluted
contaminant flux per unit plume footprint cross-sectional area with
the representative wind velocity at each sample inlet location as a
function of the representative wind velocity; deriving an emission
rate of a candidate contaminant emission source corresponding to
each sampling inlet location based on the corresponding variation
of the diluted contaminant flux per unit area, each candidate
source being presumed to be located at a parcel of land located
about the sampling inlet locations; comparing emission rate values
of a first group of candidate sources; and asserting that the
contaminant emission source is located at the parcel of land based
on a substantial agreement between a subgroup of candidate source
emission rates of the first group of candidate sources.
[0011] In accordance with another aspect of the present invention,
there is provided a method for deriving the variation of the
diluted contaminant flux per unit area comprises multiplying each
diluted concentration measurement with the corresponding
representative wind velocity determined based on weighted
contributions of a plurality of wind velocity measurements measured
prior to the diluted concentration measurement.
[0012] In accordance with a further aspect of the present
invention, there is provided a method for deriving the emission
rate of each candidate source comprising, for each sample inlet
location identifying a predominant peaked flux distribution along
the directional component of the wind velocity of the variation of
diluted contaminant flux per unit area; for each sample inlet
location characterizing the predominant peaked flux distribution
with respect to a peak flux magnitude, a prevailing direction and a
peak azimuthal width; selecting the parcel of land to have a
location defined by the locus of intersections of vectors emanating
from each sample inlet location along the corresponding prevailing
directions; and multiplying the peak flux magnitude and a plume
cross-sectional area at the sample inlet.
[0013] In accordance with a further aspect of the present
invention, the method further comprises: determining a relative
concentration for each concentration measurement at the
representative wind velocity with respect to the average variation
of the diluted contaminant concentration at the representative wind
velocity; and asserting a corresponding relative emission rate
deviation from an average emission rate at the representative wind
velocity at a previous time corresponding to the plurality of wind
velocity measurements and a measurement sampling rate.
[0014] In accordance with a further aspect of the present
invention, the method further comprises: deriving an average
variation of the diluted concentration of the contaminant with
representative wind velocity determined for at least one of the
sampling inlet locations; determining a relative concentration of
each concentration measurement at the representative wind velocity
with respect to the average variation of the diluted contaminant
concentration at the representative wind velocity; and asserting a
corresponding relative emission rate change of the contaminant
source emission rate from an average contaminant source emission
rate at a previous time corresponding to distance between the
contaminant emission source location and the sampling inlet
location divided by a speed component of the corresponding
representative wind velocity.
[0015] In accordance with another aspect of the present invention,
there is provided a method of locating at least two sources
emitting a contaminant into a moving fluid, the contaminant forming
one or more plumes, the method comprising: measuring a variation of
a diluted concentration of the contaminant at least two sampling
inlet locations about the sources, the diluted contaminant
concentration varying over time with a corresponding representative
wind velocity; determining a flux with the representative wind
velocity at each sample inlet location as a function of the
representative wind velocity; for each sample inlet location,
identifying any peaked flux distributions along the directional
component of the wind velocity of the variation of the flux;
characterizing each peaked flux distribution of each sample inlet
location with respect to a prevailing direction; for each peaked
flux distribution defining a directional vector passing through
from the corresponding sampling inlet location in the corresponding
prevailing direction; selecting a group of vectors having a vector
corresponding to each sample inlet location; and performing a
method of characterizing a contaminant source using the peaked flux
distributions corresponding to a subgroup of vectors.
[0016] In accordance with a broad aspect of the present invention,
there is provided a method of monitoring a contaminant emission
from at least one source, the method comprising: measuring a
variation of a diluted concentration of the contaminant perceived
at a sampling inlet location, the diluted contaminant concentration
varying over time with a corresponding representative wind
velocity; determining a variation of a diluted contaminant flux per
unit contaminant plume footprint cross-sectional area with the
representative wind velocity at the sample inlet location as a
function of the representative wind velocity; determining an
emission rate corresponding to the sampling inlet location by
multiplying an average diluted contaminant flux per unit area
within a predefined azimuthal width centered about a direction
pointing to a locus of the at least one emission source and an
emission plume cross-sectional area at the sample inlet
location.
[0017] It is to be understood that other aspects of the present
invention will become readily apparent to those skilled in the art
from the following detailed description, wherein various
embodiments of the invention are shown and described by way of
illustration. As will be realized, the invention is capable for
other and different embodiments and its several details are capable
of modification in various other respects, all without departing
from the spirit and scope of the present invention. Accordingly the
drawings and detailed description are to be regarded as
illustrative in nature and not as restrictive.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Referring to the drawings wherein like reference numerals
indicate similar parts throughout the several views, several
aspects of the present invention are illustrated by way of example,
and not by way of limitation, in the drawings, wherein:
[0019] FIG. 1 is a schematic diagram showing an air sample
monitoring system in accordance with an embodiment of the
invention;
[0020] FIG. 2 is a schematic diagram showing elements of an air
sample analysis system in accordance with the embodiment of the
invention;
[0021] FIG. 3 is a schematic diagram showing elements of an air
sample analysis system in accordance with another embodiment of the
invention;
[0022] FIG. 4 shows tabulated air sample analysis measurements
recorded using a stationary setup;
[0023] FIG. 5 shows tabulated air sample analysis measurements
recorded using a mobile setup;
[0024] FIG. 6 is a schematic diagram showing variables taken in to
account in correlating wind speed and direction data to
corresponding analysis measurements;
[0025] FIG. 7a, FIG. 7b, and FIG. 7c are a sequence of graphs
showing data processing in obtaining a variation of average
material concentration;
[0026] FIG. 8a, FIG. 8b, and FIG. 8c show different perspectives of
a variation of a contaminant concentration measurements varying
with wind speed and direction shown in FIG. 7;
[0027] FIG. 9a, FIG. 9b, and FIG. 9c show different perspectives of
a variation of flux per unit plume footprint cross-sectional area
plotted versus wind speed and direction shown in FIG. 8;
[0028] FIG. 10 is a graph showing contaminant flux varying with
wind direction;
[0029] FIG. 11 is a graph showing contaminant flux varying with
wind speed;
[0030] FIG. 12 is a schematic representation of the shifting of a
plume with wind speed;
[0031] FIG. 13 is a schematic representation of emitting source
location prediction by triangulation;
[0032] FIG. 14 is an example of an actual plot of predicted
emitting sources by triangulation;
[0033] FIG. 15 is a schematic representation of relevant variables
employed in quantifying the size of the leak;
[0034] FIG. 16 is a schematic representation of relevant variables
employed in determining the height of a contamination emission
source;
[0035] FIG. 17 shows two variations of flux per unit area versus
wind velocity taken from two remote sampling inlets at the same
location but at different elevations;
[0036] FIGS. 18a to 18d show exemplary graphs of contaminant
emission variability for a contamination emission source with
time;
[0037] FIG. 19 shows a map of a physical area with emission sources
detected in accordance with an embodiment of the present
invention;
[0038] FIG. 20 is a flowchart of a process according to an
embodiment of the present invention;
[0039] FIG. 21 is a flowchart of a process for performing
surveillance and gathering potentially relevant data for
determining emission sources according to an embodiment of the
present invention.
[0040] FIG. 22 is a flowchart of a process for mapping an emission
source according to an embodiment of the present invention; and
[0041] FIG. 23 is a flowchart of a process for monitoring an
emission source according to an embodiment of the present
invention.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0042] The detailed description set forth below in connection with
the appended drawings is intended as a description of various
embodiments of the present invention and is not intended to
represent the only embodiments contemplated by the inventor. The
detailed description includes specific details for the purpose of
providing a comprehensive understanding of the present invention.
However, it will be apparent to those skilled in the art that the
present invention may be practiced without these specific
details.
[0043] According to one embodiment, the present invention
comprises, as will be described in more detail below, a system and
method for locating and characterizing a material or compound (e.g.
a contaminant) emitted from one or more sources through the
collection of localized measurements (for example, by way of point
observations) of concentration data by one or more or more sensors
that could be located about an area to be monitored or adjacent to
the area and be either moving or stationary and correlating the
observed contaminant concentration data to wind speed and direction
data (or any other meteorological factors that can affect air
movement such as, but not limited to: light intensity, vertical
wind speed, temperature, etc.) in such a way as take into
consideration wind velocity (i.e. speed and direction) variability
along a path (i.e. trajectory) traveled by the emission, e.g.
airborne contaminants.
[0044] In the context of the present description, a plume means or
refers to a column or aggregation of the emitted material which
moves through the air. Plume may also refer more generally to a
column of a fluid moving through another fluid. Several effects
control the motion of the fluid, including momentum, buoyancy and
density difference.
[0045] As will be described in more detail below, plumes and their
trajectories can be identified at an observation point. In the
context of the present invention, dimensionless and dimensioned
plumes are described. Dimensionless plume boundaries and flux
patterns across the cross-sectional area within the dimensionless
boundaries can be identified. As will be described in more detail,
projecting this information back along the trajectory or path the
plume has traveled to the source location provides the capability
to predict or determine the dimensioned plume (i.e. the real
plume). The dimensioned plume has scalar dimensions that reflect
one or more characteristics of the emission, including size,
variability, elevation, buoyancy, exit momentum, plume
concentration profile and weather point, area or multiple sources.
When the location of the source is not known, knowledge of some of
its characteristics provide the capability for a single observation
point to be used to predict its location, as will be described in
more detail below. According to another aspect, multiple
observation points are utilized in accordance with the present
invention to predict unknown sources based on the agreement in the
projected plume information.
[0046] The plume boundaries and trajectory are identified in the
concentration versus wind velocity plot. The concentration versus
wind velocity plot is converted to a flux, i.e. a "flux per unit
plume footprint cross-sectional area", versus wind velocity plot.
The wind velocity data can be weighted, for example, according to
concentration of wind speed, which allows the correct
interpretation of concentrations at different wind speeds.
[0047] According to an embodiment, dimensionless plumes comprise
angular dimensionless measures of the plume boundaries and the flux
pattern across the plume is determined at the point of observation.
Dimensionless angular width is determined from the peaked flux
distribution (for example, as described below for FIG. 10).
Dimensionless angular height can be determined either from weighted
conversion of wind speeds (for example, as described in FIG. 11) or
assumed to be related to the width, or just simply assumed. The
boundaries are projected outward along trajectory or path the plume
traveled back to the source (not necessarily linear due to curving
wind and or obstacles). The dimensionless boundaries are converted
to boundaries with scalar dimensions (feet or meters) with the size
directly related to distance projected. For example, plume width
can be calculated as plume width=sine (dimensionless angular
width)*(distance between observation point and projection), and
plume height can be calculated in a similar manner.
[0048] When projected back the proper distance along the trajectory
or path to the source, the true physical size of the plume can be
predicted, for example, assuming elliptical or rectangular plume
shape, depending on a point source, or an area source or multiple
sources. The concentration and flux pattern within the plume
boundaries pattern and the "flux per unit plume footprint
cross-sectional area" pattern across the plume cross-section can be
converted to scalar units, i.e. feet or meters.
[0049] According to an aspect, the size of the emitting source can
be determined by combining the flux within the plume boundaries
less the background flux level measured outside the plume boundary
with the area of the plume determined from the scalar dimensions
above.
[0050] According to an aspect, the variability in the emission rate
of the source can be determined by comparing concentrations
measured within the plume boundaries to the longer term average
concentration pattern profile within the plume boundary, and
attributing deviations from the long term average profile to source
emission rate changes.
[0051] According to an aspect, the elevation of an emitting source
can be determining by projecting the trajectory of a plume in the
vertical plane back to the source location from one or more
observation points.
[0052] As will be described in more detail below, by asserting one
or more candidates for potential emitting sources, and then
determining one or more associated characteristics, i.e. size,
variability, elevation, buoyancy, exit momentum, plume
concentration profile and weather point, area or multiple sources,
by projecting one or more plumes back along one or more plume
trajectories (i.e. corresponding to one or more observation
positions) provides the capability to predict the presence of one
or more emitting sources based on the agreement, e.g. convergence,
of the one or more candidates for potential emitting sources.
[0053] In addition to plumes or segments of plumes, individual
readings or groups of readings (and associated flux per unit
cross-sectional footprint area) can be projected back along the
trajectory or path traveled from a single, multiple or mobile
observation points to determine one or more potential emission
source locations. The existence of one or more emission sources can
be asserted, i.e. determined, based on agreement from the
observations, e.g. multiple observations from a single position
over time, or observations from multiple positions or observations
from mobile points.
Sampling Setup
[0054] In accordance with an embodiment of the invention, air
concentration measurements of a particular emission, for example,
an air contaminant, can be collected at multiple locations about an
area of interest. Multiple measurements can be collected by using a
single sensor, multiple stationary sensors, one or more moving
sensors, or one or more stationary analyzers that draw air samples
from spaced apart locations. The area being monitored can vary from
very small in the range of 10's of meters to very large, e.g.
greater than 10's of kilometers (the range limit is unknown). The
larger the area to be monitored, the more the air contaminant
concentration measurement locations have to be spaced from each
other to improve triangulation. For certainty, while the locus of
the air contaminant concentration measurements defines an inner
area, spacing the measurement locations from each other, also
enables detection of contaminant sources outside the locus.
[0055] FIG. 1 shows an air sample monitoring system 100 having an
equipment package 110 servicing a group 120 of sample inlets 130 in
accordance with the embodiment of the invention.
[0056] Air samples are drawn from each sampling inlet 130 down
sample lines 132 which may include small diameter tubing (plastic
or the like), to the equipment package 110. The sample inlets 130
can be positioned anywhere with respect to the equipment package
110. However, the cumulative friction between the air samples
within the sample lines 132 and the sample line walls may limit the
length of sample lines 132. Also different compounds have different
wall adhesion largely depending on compound molecular size and
weight, and therefore different compounds take a slightly different
time to travel along a sample line. The actual time of travel down
a sample line can be determined empirically.
[0057] FIG. 2 shows elements of an implementation of an air sample
analysis system in accordance with the embodiment of the invention,
the equipment package 110 includes: [0058] sample lines 132
configured to convey air samples from corresponding sampling inlets
130, each sampling line 132 may include: [0059] a filter 140
configured to filter out debris (filter could be put at inlet end
of sample line to keep debris out of sample line as well); [0060] a
(needle) valve 142 configured to control the rate air intake; and
[0061] a flow rate sensor 144 configured to provide a flow rate
output for valve 142 adjustment; [0062] a zero line 134 configured
to supply zero gas; [0063] a span line 136 configured to supply gas
having a know concentration of the target contaminant to be
detected; [0064] a valve manifold 150 configured to couple a
selected air sample flow from one of the sample lines 132 to a
sample analysis stream 160; [0065] a vacuum source 152 configured
to draw air samples via the air sample inlets 132 and thereby to
convey the air samples to the equipment package 110; [0066] an flow
meter 162 configured to measure the flow rate of the air sample in
the analyzer stream 160; [0067] at least one sample analyzer 164
configured to receive an air sample from the sample analysis stream
160 and to perform a measurement on the air sample, such as but not
limited to, measuring the concentration of a particular compound
(the sample analyzer may include its own pump); and [0068] a
controller 166 configured to actuate valves of the valve manifold
150, activate the at least one analyzer 164, receive and log data
including, but not limited to: an indication of which air sample
stream is being analyzed in the sample analysis stream (valve
position), sample flow rate, concentration, sample analysis time,
wind speed and wind direction.
[0069] In accordance with an implementation according to an
embodiment of the invention, the controller 166 is further
configured to log sampling times. Due to non-trivial lengths of the
sampling lines 132, the controller 160 may be further configured to
take into account the propagation delay of each air sample along
the sample line 132. For such a purpose, the controller 160 may
employ the flow rate output provided by the flow meter 162, the
inner diameter of the sample line 132 and the length of the sample
line 132 to provide an estimate of the time of travel of each air
sample from the sample inlet 130 to the sample analyzer 164 at the
equipment package 110.
[0070] For continued air monitoring applications, a fresh air
sample may be necessary for each air sample analysis performed by
the sample analyzer 164. Particularly in view of long sample lines
132, fresh air samples can be applied to flush or otherwise remove
stagnant air in the sample line. When multiple sample lines 132 are
connected to the equipment package 110, a fresh air sample may only
be required during the time period when each sample line 132 is
selected and connected to the sample analysis stream 160.
[0071] Without limiting the invention, having fresh air samples for
analysis may be achieved, for example, by continuously drawing air
samples to avoid the time delay to clear the sample line 132, or by
initiating the drawing of an air sample sufficiently in advance of
the sample analysis to clear the previous sample from the sample
line 132. When not connected to the sample analysis stream 160,
sample lines 132 may be connected to the vacuum source 152 to
maintain a constant flow to deliver fresh air samples down the
sample lines 132 while each sample line 132 waits its turn to
deliver an air sample to the sample analysis stream 160, which may
deliver the sample at a constant flow rate. The vacuum source can
include a vacuum pump, and the length of the sample lines 132 may
be limited by the ability of the vacuum pump to draw air samples
through the sample lines 132.
[0072] Without limiting the invention, each sample line 132 can be
selected in accordance with a selection discipline, via the
selection valve manifold 150, to be coupled to the sample analysis
stream 160. Depending on the particular implementation, the valve
manifold 150 can include solenoid valves, for example, a series of
2/2 way valve pairs, or a series of 3/2 way valves.
[0073] Optionally, the flow rate meters 144 may include rotameters
employed to provide visual confirmation of air flow down the sample
lines 132.
[0074] The equipment package can be retrofitted to existing air
monitoring equipment by connecting the sample analysis stream to
the existing air monitoring equipment.
Second Prototype of Hardware Package
[0075] For some configurations of the equipment described above,
the assumption of constant and stable flow in the sample lines 132
may not hold, and measuring only the flow rate in the sample line
132 that was delivering a sample to the sample analysis stream 160
may not be completely accurate. Therefore adjustments may be
necessary for any flow rate changes that may occur on the sample
lines 132 during valve changes or longer term declines in flow
rates due to pump wear-out.
[0076] FIG. 3 shows elements of another embodiment of the equipment
package 110 which includes: [0077] a multi-port valve 154 (for
example VICI valve number "H-EMT2SC16MWE" available from VICI Valco
Instruments Canada Corp., 26 Water Street East, Brockville, ON K6V
1A1, Canada, Phone: (613) 342-2600, Toll free: (866) 297-2626, Fax:
(613) 342-0111, canada@vici.com, www.vici.com) having inputs to
which at least some of the following connect: [0078] the sample
lines 132, [0079] the zero line 134, and [0080] the span line 136;
[0081] and outputs including: [0082] a common bleed line connected
to the vacuum source 152, and [0083] a line leading to the sample
analysis stream 160; [0084] a sample analysis stream pump 168
configured to draw an air sample via the sample line 132 selected
via the multi-port valve 154 to be connected to the sample analysis
stream 160; [0085] signaling lines 146 connecting each flow sensor
144 to the controller 166, the controller 166 being further
configured to adjust valves 142 to match sample line flow rates to
a common flow rate selected for all the sample lines, for example
to match the flow rate of the sample analysis stream pump 168;
[0086] solenoid valves 148 on the zero and span lines configured to
shut off the zero and span gases when not in use to conserve zero
and span gases (as opposed to the air samples, the zero and span
gases do not vary in concentration).
Remote Sampling
[0087] In accordance with the embodiment of the invention,
locations for a group of sampling inlets 130 about the area of
interest are selected. According to an embodiment, the sampling
locations are separated from each other to provide for
triangulation in locating contaminant sources that may exist within
or without the air-sampling locus.
[0088] Sample inlets 130 positioned at several elevations may be
employed; at least one sample inlet positioned at least as high as
the elevation of the highest potential leak may ensure that
emission plumes would not pass by the sample inlets 130
undetected.
[0089] In accordance with an implementation according to an
embodiment of the invention, remote sampling couplets can be
employed. Each sampling couplet can include at least two sampling
inlets 130 preferably having a broad geographical separation and
coverage of the area of interest. Accordingly sub-groups of groups
of sampling locations may be established which provide coverage for
different sized monitored areas and at different distances. For
example, for large contaminant emissions released at great
distances from the sample measurement locus, each couplet functions
as a point measure, while at the same time providing surveillance
for smaller contaminant leaks released closer to the sample
measurement locus.
Calibration
[0090] As an initial step, a calibration of the analyzer 164 is
performed. The analyzer 164 can also be calibrated at selected
interval (for example, once a day). The calibration involves
providing a (gaseous) sample that is known to be free of the
compound to be detected and recording the analyzer's response
(called the zero reading) followed by the provision of another
sample containing a known concentration of the compound to be
detected and recording the analyzer's response, i.e. the span
reading.
[0091] In order to obtain the zero response, the valve manifold 150
or the multi-port valve 154 and valve 148 on the zero line 134 are
configured to connect the zero line 134 to the sample analysis
stream 160 and turn the zero gas on. In order to obtain the span
response, the valve manifold 150 or the multi-port valve 154 and
valve 148 on the span line 134 are configured to connect the span
line 136 to the sample analysis stream 160 and turn the span gas
on.
[0092] Calibration adjustment factors are obtained by comparing the
recorded zero and span responses of the analyzer 164 to true
values, for example, 0 and 100 ppm. The instruments response can be
corrected internally to the zero and span reading.
Data Collection
[0093] Data collection follows calibration, which can occur
periodically during data collection. FIG. 4 shows tabulated raw
data collected every 10 seconds employing a stationary sensor setup
wherein the location of the sample inlets 130 does not change
during the data collection. Valve positions correspond to
pre-assigned sample inlet locations in terms of latitude, longitude
and elevation for stationary sample inlets 130. FIG. 5 shows
tabulated raw data collection using a (mobile) sensor setup, each
entry including: latitude, longitude, and elevation. A log of wind
speeds and directions are kept, from which a previous wind speed
measurement and a corresponding wind direction measurement may be
selected based on the air sample propagation time delay down a
corresponding sample line 132, valve manifold 150, sample analysis
stream 160 and possibly through at least a portion of the analyzer
164.
[0094] The invention is not limited to a 10 second data collection
interval, data collection frequencies may range from less then 0.5
seconds to hours. The concentration data readings are averaged
during an interval of sampling to reduce signal noise and possibly
analog to digital conversion errors. For example, measurements can
be made at a frequency of 500 readings per second collected and
averaged over the 10 second period. Contaminant concentration can
be measured in parts-per-million (ppm). The concentration data
collected during a calibration interval can be adjusted with the
calibration readings obtained for that calibration interval either
by the sample analyzer 164 or as a post-measurement processing step
performed by the controller 166.
[0095] As valve positions change for a different sample line 132 to
be connected to the sample analysis stream 160, a transition period
is employed during which at least the sample analysis stream 160,
and the analyzer 164, may be purged of the previous air sample held
therein. Any readings taken during the transition period are
discarded, or measurement reading may be paused for the duration
thereof. Any sampling strategy to improve the detection limit of
the detectors can be used like drawing a large sample through an
absorbant and then periodically desorbing.
Calculating Wind direction
[0096] In tracking emission sources an accurate characterization
may be needed of air movement (wind driven) driving contaminant
plumes from emitting sources to the air sample inlets 130 at
corresponding measurement locations. In accordance with the
embodiment of the invention, wind speed and wind direction are not
assumed constant, and actual wind speed and wind directions are
included in determining the location of contaminant sources. As
described above, wind speed and direction can be measured at each
air sample inlet 130 location or at a reduced number of locations.
Following the measurement of wind velocity characteristics such as,
but not limited to: wind speed and direction readings, the wind
characteristics are correlated with the corresponding concentration
readings performed by the at least one analyzer 164. Correlating
wind characteristics can take into account air sample travel time
along the sample lines 132 from the air sample inlet 130, and time
of travel over the area of interest.
[0097] By way of example and without limiting the invention, wind
speed is provided by a wind meter as the rate of rotation of a
wheel spun by the wind, and wind direction may be provided by the
output of an encoder encoding the orientation of a wheel connected
to a wind vane. Both outputs may be provided to the controller 166
as described above, typically as electronic signals whether analog
or digital. Depending on atmospheric conditions, vertical wind
speed is also taken into account.
Wind Direction Correction
[0098] The accuracy of the triangulation may depend on the accuracy
of the wind direction measured. Several aspects of wind direction
may need correction: internal errors of the wind meter, external
errors of the wind sensor and possibly adjusting the reading from
the output range to the zero-to-360 range.
[0099] Internal corrections: for a wind sensor employing a
potentiometer to encode the direction of the wind vane, when the
wind vane is pointed at North there can be internal errors in the
positioning of the potentiometer and other electronic components
that measure direction which would result in the output not
equating 360 degrees. There are additional errors that can result
in the electrical signal (current or voltage) from the wind sensor
and recorded in the data file. The correction needed is determined
by connecting the wind sensor in the field to the length of cable
and data logger that will be used in the setup (this ensures that
any shifting or degradation of the electrical signal are included
in the calibration) and then the wind vane positioned at the know
positions of North, South, East, and West as accurately as
possible. Some wind sensors use a dual potentiometer encoding
system that records wind direction from 0 to 540 degrees, in this
case the sensor may be rotated to record the output at each 90
degree interval (i.e. 0, 90, 180, 270, 360, 450, and 540). A
correction value is determined by plotting the expected and the
actual readings, this correction typically has the form y=mx+b and
in some situations it is possible that m will be 0 so the
correction will be a constant that is applied to all the
readings.
[0100] External Correction: the external correction is provided
because when positioning and securing the wind sensor in the field
it is impossible to have it pointing exactly in the right direction
and an adjustment is generally required. The size of the adjustment
needed is determined by sighting along the vane at some distance
(100's of meters) and finding where the North (and/or South)
direction is pointing and measuring the distance off true north or
south and calculating the number of degrees required to correct the
readings. This correction may typically be a constant
correction.
[0101] Adjustment: if the readings beyond 360 degrees are reported,
these may be converted or adjusted back to the 0-to-360 range.
[0102] FIG. 6 summarizes the wind measurement assignment
calculation required to correlate the wind speed and direction data
to the corresponding concentration readings.
[0103] The wind speed and direction is not stable over time and can
vary second to second moving a volume of air 200 along a nonlinear
path 210 from an area of interest monitored for the existence of a
contaminant source to a sample inlet 130. Obstructions such as land
topography and buildings can cause wind to be non-linear, and
knowledge of the geometry of such obstructions can improve the
tracking of the trajectory of the air. Accordingly, wind speed and
direction estimates are related to individual readings from the
analyzer 164. The frequency of readings can be as high as once
every second or every half second because it was found that plumes
are being shifted by the wind effects at this time scale. The
analyzer readings reflect contaminant compound concentration in a
volume of air 202 that has traveled through the analyzer 164, down
the sample line 132, aspired from the volume of air (200 that has
been blown over the area of interest 230 entraining molecules of
the contaminant compound emitted from unknown contaminant emission
source(s) in the area of interest 230. Readings from the analyzer
164 are related to wind sensor readings.
[0104] In accordance with an embodiment of the invention, a higher
level process is provided to account for the wind variability by
back tracking the nonlinear path 210 of the volume of air 202 from
the sample inlet 130 back over the area of interest 230, by
stepping backward in time and outward in space away from the sample
inlet 130 adjusting the path and concentration with each step for
the changing wind conditions (note concentrations would be adjusted
to reflect the dispersion that occurs as the plume travels down
wind). Each contaminant concentration measured by that analyzer 164
takes into account the degree to which changes in wind velocity
have affected the air sample traversing the path 210 outward and
up-wind from the sample inlet 130. The concentration of a
contaminant increases along path 210 as it is traversed backwards
towards the source due to the (natural) dispersion that occurs. A
mapping technique lays out a high number of paths generated with
high frequency sampling (i.e. roughly 100 thousand per day, per
sample inlet 130 location at one second sampling) from multiple
remote sampling inlets 130. Combining all the paths that traverse
through each plot of land being mapped and averaging concentrations
along such paths 210 corresponding to the all the paths from the
different sample inlets 130 can be employed to predict the
location(s) of contaminant emitting source(s). Paths 210 with high
compound concentrations congregate at plots of land that contain
emission sources.
[0105] In accordance with another embodiment of the invention, and
as a simplification of the above a representative wind velocity is
employed wherein the non-linear path 210 can be replaced with the
linear vector 212 which estimates the average wind (velocity) speed
and direction during the time of travel of the volume of air 200
from area of interest 230 to the sample inlet 130. A measure of the
standard deviation of wind speed and direction is also calculated
to provide an estimate of the accuracy of the assumption of
linearity of the non-linear flow path 210. The linearity assumption
can have more error at low wind speed because of longer averaging
times and possibly due to a more unstable direction of flow of the
wind (i.e. low speed wind may be subject to more radical changes in
direction than high speed wind. In addition the travel time, which
is calculated as the distance 214 over wind speed, increases
dramatically as a function of reciprocal wind speed and at low
speeds (i.e. air moving at low speed takes much longer to get to
the sampling inlet 130 and results in a longer averaging time
substantially equal to the traveling time). The result of standard
deviation calculation is used to filter out readings of the
analysis that occur when the wind direction shifts too much for an
accurate prediction of the flow path 210/212. This technique
identifies wind data that accurately predicts wind effects and
eliminates data that does not. Accurate low wind data may be very
valuable in locating emission sources at great distances if the
wind direction is stable. With knowledge of the geometry of the
topography, buildings and other obstacles, the trajectory of the
plume can be assumed linear and corrected for movement around
obstacles. With this technique, an estimate of the distance 214
from the area of interest to the sample inlet 130 is used to
determine the duration of averaging needed. It was found that the
distance to the monitored area may be fairly rough. An accuracy
(.+-.50%) of the assumed distance does not seem to have an
important impact on the prediction of the average representative
wind velocity because; at high wind speed the wind direction is
more stable, and at low wind speed because averaging times are so
long that the variability in low wind direction averages out. A
rough first guess of the distance can be used to identify sources
and then a much better distance estimate to each source obtained.
In an iterative fashion, the data can be analyzed again using the
better distance measures to each source to focus the analysis
better (maybe even increasing the wind direction increments to one
tenth of a degree accuracy in the follow up runs). This focusing
can serve to improve the location, sizing and characterizing of the
sources.
[0106] In accordance with another embodiment of the invention, an
iterative approach is provided, wherein a rough initial estimate of
distance is used to establish a first estimate of the distance to
emission source(s) and then a more accurate distance is calculated
and used in another run.
[0107] As a subsequent step, an adjustment is made for the time of
travel of the air sample 202 from the sample inlet 130 location
down the corresponding sample line 132 through the equipment
package 110 and to the analyzer 164. The time of travel of the air
sample 202 down the sample line 132 may be measured empirically by
aspiring a sample 202 of known concentration at the sample inlet
130 and timing the length of time need for the analyzer 164
readings to respond with the arrival of the air sample.
Alternatively the internal volume of sample line 132
(cross-sectional area of the sample line multiplied by the length
thereof) can be divided by the sample flow rate drawn through the
sample line 132 and adding the time needed for the analyzer 164 to
respond to a sample that enters the analyzer 164. The time of
travel of air samples down the sample line 132 are typically unique
to each valve position.
[0108] In accordance with an implementation the analyzer 164,
contaminant concentration readings are related to wind speed and
direction at a corresponding inlet 130 as follows: [0109] each
concentration reading relates to a representative wind vector (t-t1
to t-(t1+t2)) given that: [0110] 130--Remote sample inlets [0111]
200--Hypothetical volume of air traveling from area of interest to
remote inlet. [0112] 210--Non-linear path of the volume of air
driven by the wind [0113] Ws--The wind speed [0114] Wd--The wind
direction [0115] 212--Average wind vector (speed, direction) during
travel time (t-t1 to t-(t1+t2)) [0116] 164--Analyzer detector
[0117] 214--Distance from area of interest to remote sample inlet
[0118] 132--Sample tubing running from remote sample inlet 130 to
analyzer 164 [0119] 230--Area of interest [0120] t--Current time
[0121] t1--travel time of sample from the inlet 130 to the sample
analyzer 164=(sample tube volume)/(flow rate) [0122] t2--travel
time of 200 from area of interest 230 to the sample in 130 divided
the representative wind speed
Populating the Table of Wind Speeds and Directions
[0123] In a subsequent step the variation of concentration of the
contaminant with wind speed and direction is determined. FIG. 7
shows concentration readings plotted against wind speed and
direction in a three-dimensional plot. FIG. 7 shows an average
surface through the cloud of data, and FIG. 8 shows average
concentration surface without the data points. The average (or
median) contaminant concentration variation surface reflects plume
characteristics, the figure showing the association of low wind
conditions with high contaminant concentrations, and contaminant
concentration decreasing with increasing wind speed unless the wind
is blowing in the direction of a contaminant source, in which case
contaminant concentration decreases with separation distance and
increases with wind speed.
[0124] Each mapped contaminant can have a corresponding surface for
each sampling inlet location. For example, drawing air samples at
six sampling inlet locations and employing three analyzers 164
measuring corresponding contaminant concentration's would result in
18 such graphs. For example, the surface shown in FIG. 8 was
generated from data collected over 5 weeks worth of measurements
averaged over 10 second intervals and plotted.
[0125] Making reference to FIG. 6, concentration measurements are
converted into a surface.
[0126] As an initial step, for each compound analyzed and for each
remote sample inlet a set of bins/registers is defined, bins which
store concentration data measurements for every combination of wind
speed and direction. For example if wind between 0 to 50 kph is
considered using a resolution of one kph increments and wind
direction from 0 to 360 degrees using one degree increments, then
18,000 (360*50) registers or bins to allow for every combination of
wind speed and direction.
[0127] Each recorded reading from the analyzer 164 is assigned to a
bin based on the contaminants measured, the remote sampling
location, and the representative wind speed and direction that
affected the volume of air that the sample was drawn from as it
traveled over the area of interest to the remote sampling inlet.
The data sets generated previously are stepped through one record
at a time and each concentration reading is assigned to the
appropriate bin.
[0128] Digital values of the surface at all wind speeds and
direction are obtained by averaging (or median) the concentration
measurements accumulate in the bins. An adequate (large) number of
concentration measurements are required in each bin to obtain an
average and digitized surface due to the unstable nature of the air
concentration measurements. This unstable nature is evident in the
earlier FIG. 8 where the average level in the surface is plotted
along with individual measurements wherein the individual
concentration measurements are shown scattered well above and below
the average concentration surface.
Adjusting the Digital Surface
[0129] The adjustments may need to be made to the average
contaminant concentration surface on the assumption that at a low
resolution, the true surface is continuous and smooth without step
changes in concentration with varying wind speed and direction. The
surface should be generally continuous due to the dispersion of
contaminant in the emission plumes as the air travels from the
contaminant emission source to the sampling inlet location. A
reasonable assumption because essentially stating that the
concentration at one wind speed and direction is linked to the
concentration at the same wind speed but one degree difference.
This means that the true concentration measured in respect of a bin
should be similar to that of the neighboring bin.
[0130] Based on the above assumption, readings stored in
neighboring bins of the registry can help predict the true valve of
the surface at a bin. The adjustment combines the readings stored
in a bin with the measurements held in neighboring bins (this is
done via preset adjustable intervals, for example, three bins along
increasing and decreasing wind direction and four bins in along
increasing and decreasing wind speed). The larger the interval the
more smoothing of the true surface will occur. Smoothing can reduce
the noise in the data but may also round off any sharp
characteristics in the actual surface.
[0131] In order to reduce deleterious smoothing of the true surface
that occurs with the above adjustment, one can employ average
weighting techniques taking into account proximity of bins being
considered giving more influence to neighboring bins which are
closer in wind speed and direction to the bin in question.
[0132] In order to characterize contaminant emitting sources only,
the background concentrations of the contaminant monitored are
subtracted from the average concentration surface.
[0133] Calculating Flux Per Unit Area
[0134] Flux is fluid flow past a surface. An emitting source
generates a contaminant plume driven by the air movement and
expanding due to dispersion. As the plume crosses a boundary, the
rate of contaminant flow or flux of the contaminant through the
boundary corresponds to the emission rate of the contaminant as
long as the emission rate is constant assuming that contaminant is
not being created nor destroyed as it travels. The flux of a
contaminant plume is employed to quantify the emission rate of a
corresponding emitting source. The product between contaminant
concentration and wind speed give an indication of flux per unit
area. If the area of the plume passing the boundary was known, then
flux=concentration*wind speed*plume area. Each plume may be
approximated with a suitable shape, for example, a conical plume,
in nature not necessarily of a circular perpendicular
cross-section.
[0135] Therefore a corresponding average flux per unit area surface
is derived from the average contaminant concentration surface shown
in FIG. 8 by multiplying each contaminant concentration value by
the corresponding wind speed. FIGS. 8 and 9 illustrate exemplary
plume boundaries, trajectories and flux and concentration profiles,
and FIGS. 10 and 11 provide a simplified depiction of FIG. 9.
[0136] In order to characterize contaminant emitting sources only,
the background concentrations of the contaminant monitored are
subtracted from the average concentration surface before the flux
per area is calculated.
Calculating Vector Plots
[0137] This step involves averaging the flux per area table for
each increment of wind direction.
[0138] This will result in one estimate of the flux per area or
plume footprint cross-section value for each wind direction and can
be plotted as shown in FIG. 10. As shown there are certain wind
direction with near zero flux/area values (no sources intercepted
(the plume may go over head) in those directions) and other
directions with elevated levels (sources in those directions).
Typically predominant peaks in the graph correlate with the
direction from the sample inlet location to the source of an
important emission source. The central line (at 265 degrees)
reflects the plume trajectory or direction to the emission source
that causes the largest peak. The edges of the peak, marked with
dashed lines, generally correspond to the side boundaries or edge
of the plume; for example the width of the plume is measured at 39
degrees (this is the dimensionless angular width of the plume).
[0139] In accordance with an embodiment of the invention, at least
one predominant local peaked flux distribution along the
directional component of the wind velocity is identified on the
plot of the variation of diluted contaminant flux per unit area.
Each local peaked flux distribution is characterized with respect
to a peak flux magnitude, a prevailing trajectory or direction and
a peak azimuthal width or dimensionless angular width.
Dimensionless angular height and other characteristics are also
determined, for example, as described in more detail below.
[0140] Signal to Noise Rejection analysis may be performed to
identify peaks in the flux data from the background or noise.
[0141] Characterizing each predominant peaked flux distribution may
include fitting the peaked flux distribution to a peaked
distribution function. Peaked distribution functions include, but
are not limited to: a normal distribution, a Gaussian distribution,
a lambda distribution, an exponential distribution, or a step
distribution. For example, for a Gaussian distribution the
prevailing direction is midpoint of the Gaussian distribution and
the azimuthal (angular) width is between one sigma and three sigma
out from the midpoint, and the peak magnitude is height of the
fitted Gaussian distribution. As another example, for a lambda
distribution the prevailing direction is the median, the width is
between one sigma and three sigma out from the median, and the peak
magnitude is the height of the fitted lambda distribution.
[0142] Where fitting is not employed characterizing the predominant
peaked distribution includes selecting a pair of flux values to the
sides of the predominant peak of the distribution subject to a
minimum threshold flux value above the ambient diluted contaminant
flux and setting the prevailing direction as the average direction
of the pair of flux values.
[0143] Alternatively, characterizing the predominant peaked
distribution of the directional variation of diluted contaminant
flux per unit area includes selecting a pair of directional flux
values to the sides of the predominant peak of the distribution of
the directional variation of diluted contaminant flux per unit area
within a threshold above the ambient diluted contaminant and
setting the prevailing direction as the median direction of the
flux variation between the pair of flux values.
Allowing for Differences Along Wind Speed and Direction Axes
[0144] FIG. 10 is a plot showing average flux variation with wind
direction, where flux values at all wind speeds are averaged and
plotted against wind direction. Examining the predominant peak that
appears in the vector plots in FIG. 10 (between 244 and 285
degrees) with respect to wind speed provides further insight into
characteristics of the plume. FIG. 11 shows a plot of the sum of
flux/area values between 246 and 285 degrees plotted against wind
speed. The graph shows the plume's footprint at the sample inlet
130 at different wind speeds. FIG. 12 shows a pictorial
representation of how the position of a plume may be shifted in the
vertical direction by the wind for buoyant contaminants and
resulting in air samples of different contaminant concentrations
being aspired at a stationary sampling inlet 130. The FIG. 11
predicts that the lower edge or boundary of the plume is crosses
the sampling inlet 130 at a wind speed of roughly 7 kph and the
upper edge or boundary of the plume crosses at a wind speed of 30
kph. The center of the plume crosses at a wind speed of 20 kph.
FIG. 11 shows the maximum flux/area value of 0.339 103 m3/yr/m2.
These boundaries in terms of wind speed can be converted to a
dimensionless angular plume height, for example; as described
below. As noted the plume height can be calculated or assumed (also
assumed related to the width).
[0145] The plume width in FIG. 10 may likely be different at
different wind speeds. This is to say that as one moves along the
curve in FIG. 11 the plume width may change (typically narrower at
higher wind speed, however at very low calm winds may have narrower
plumes than some higher wind conditions as the flow may be more
laminar).
[0146] The average contaminant concentration levels versus wind
speed for the wind direction that coincides with a significant
source can provide valuable insight into the contribution the
emission source has in low wind conditions. This can be useful for
compounds where high concentrations will cause problems for
example, hazardous contaminants, odor contaminants, or flammable
contaminants. Plots of the concentration versus wind speed in the
direction of important leaks may be useful. These are produced from
column averages between the wind directions of a plume from the
adjusted average concentration plots, for example, as depicted in
FIG. 8.
Triangulating to Locate Leaks
[0147] The directions or trajectories to the important sources
identified by predominant peaks in the vector plot(s) FIG. 10 are
projected outward from each of the sample inlet locations.
Somewhere along the line of each projected vector (prevailing wind
direction representative of the peak flux distribution) may be an
important emission source. Vectors from the different sampling
inlet locations may cross in the vicinity of the contaminant
emission source. FIG. 13 shows schematically the prediction of the
location of a contaminant emitting source by triangulation from two
sample inlet locations. Because multiple vectors are projected from
each sample inlet location, some vectors will also cross at
locations that are not leaks (ghost leaks). When more than two
sample inlet locations are employed confidence in predicting leak
locations increases if three or four vectors crossing at a parcel
of land. In accordance with an embodiment of the invention, an
emission rate of a candidate contaminant emission source
corresponding to each sampling inlet location is derived based on
the corresponding variation of the diluted contaminant flux per
unit plume cross-sectional footprint area, each candidate source
being presumed to be located at the parcel of land. Emission rate
values of a group of candidate sources are compared. And the
location of the contaminant emission source at the parcel of land
is asserted based on a substantial agreement between a subgroup of
candidate source emission rates of the first group of candidate
sources. The subgroup can include the entire group, and the
assertion may be made when the number of candidate source emission
rates agreeing surpasses a threshold, for example, based on
emission characteristics as described above.
[0148] A map of the area being monitored may also be correlated
with the sample inlet locations. FIG. 14 shows actual results from
a facility who's predicted emitting source locations were
triangulated from the sample inlet locations. Squares indicate
estimated leak locations and circles indicate confirmed leak
locations. When a sample inlet location or observation position
does not detect plume(s) in a given direction, this is evidence
that the corresponding area(s) related to the observation direction
or coverage area are free of emission sources.
Quantifying Individual Emission Sources
[0149] With the location of an important emitting source predicted
above in FIG. 13, an estimate of the emission rate of the
corresponding leak is obtained from the data collected from the
sample inlet(s) 130. A summary of the individual leak
quantification for this leak is shown in the example depicted in
FIG. 15 and is described as follows: [0150] the distance from the
important leak (1088.8, 136.1) and the remote sample inlet (1193.5,
148.1) is calculated using trigonometry
[0150] distance=a=((x2-x1) 2+(y2-y1) 2) 0.5.
a=((1088.8-1193.5) 2+(136.1-148.1) 2) 0.5=105.4 m [0151] the
physical width of the plume at the remote sampling inlet is
determined by width=c=(sin(plume width in degrees from FIG.
10)*a
[0151] c=sin(39)*105.4 m=66.3 m [0152] area of plume is calculated
assuming a circular cross-section area=pi*(width/2) 2)
[0152] area plume=pi*(66.3/2) 2=3452 m2 [0153] the emission rate is
flux/area*area of plume where the flux/area value is determined
from FIG. 11 (wind speed versus flux/area) and is taken as the
maximum value (0.339 103 m3/yr/m2). The maximum value is used
because this will be the wind speed were the center of the plume is
intercepted by the remote sample inlet and the values plotted in
FIG. 11 are basically the integral of the flux/area values across
the center of the plume which when multiplied by the plume area
will yield the true emission rate.
[0153] Emission rate=3452 m2*0.339 103 m3/yr/m2=1171 103 m3/yr
The Actual location and emission rate of this leak was determined
to be (1084.0, 134.5) and 1035 103 m3/yr.
[0154] In another embodiment of the technology, the shape of the
plume is not considered to be circular. There are many reasons for
the shape of the plume not to be circular including non uniform
momentum at the point of release, dispersing in noncircular way
because of gravity and other dispersion forces, and the plumes may
not be circular if the source is an area source (city, tailings
pond, etc) or as a result of multiple sources combining.
[0155] As described above, the dimensionless angular plume width is
projected outward to the correct distance to the source to obtain
the scalar dimension of the plume width (horizontal plane).
Similarly, the dimensionless angular height of the plume can be
projected outward at the correct distance to predict the true
scalar dimension of the height of the plume.
[0156] A flux versus wind speed plot of a plume, for example, as
depicted in FIG. 11, along with the plume vertical velocity (due to
buoyancy or momentum) is used to predict the vertical angle of the
plume. Combining the vertical plume speed with the wind speed at
which the midpoint, leading and trailing edges of the plume are
intercepted gives three vectors. The relationship of these vectors
can be used to predict the dimensionless angular height of the
plume. When projected at the correct distance (i.e. the true
distance to the source) this will provide the actual scalar
dimensioned height of the plume that will be useful in predicting
source characteristics.
[0157] It will be appreciated that being able to calculate both the
width and height of the plume independently enables the prediction
of plume shape and better quantification of sources with non
circular plumes. This is also useful for area sources.
[0158] It will be appreciated that there can also be some error.
For example, the plume size changes with wind speed so when the
leading edge is intercepted it will be a larger plume than when the
trailing edge is intercepted. The center of the plume is also
detectable (FIG. 11 and FIG. 17) as well as the shape of the plume;
with some lab confirmation work parameters can be developed to
predict the vertical profile of the plume. For example, it may be
that the angle between the leading edge and the trailing edge of
the plume is the most predictive of vertical dimensions of the
plume. The shape of the curves in FIGS. 11 and 17 may be
predictable which would enable measuring of just a portion of the
figure and extrapolating the rest of it in situations when the
plume is not completely intercepted.
[0159] When plumes impinge on the ground the concentration profiles
will be different and not symmetrical. For example, a plume coming
off a city will have a trailing edge but not necessarily a leading
edge. This technique will be able to characterize and plume
regardless of shape and concentration profile. Observation points
at different elevations will be helpful in determining vertical
concentration profiles as well because this does not require the
plume to move in the vertical plane (due to buoyancy or momentum)
like using a single observation does
[0160] The following is an example calculation of the dimensionless
angular plume height. From FIG. 11, the mid point, leading, and
trailing edge of the plume is intercepted at wind speeds of 20, 7,
and 30 kph (5.6, 1.9, and 8.3 m/s). The plume source and the
observation point were located 105 m apart horizontal distance and
27.7 m apart vertical distance. The vertical plume velocity is 1.53
m/s (from source elevation calculation). The angles of the mid
point, leading, and trailing edge of the plume are (arc
tan(vertical velocity/horizontal wind speed) 15.4, 38.2, and 10.4
degrees. The leading edge minus the trailing edge gives 27.8
degrees height of the plume (note the dimensionless angular width
of the plume was calculated at 39 degrees). The leading edge to
midpoint is 22.8 degrees while the midpoint to the trailing edge is
5.0 degrees. The plume width is likely changing with wind speed so
obtaining the correct angular dimensionless height may require a
weighting based on the shape of the flux versus wind speed curve.
With controlled studies knowing the vertical dimension of the plume
one could develop the appropriate weighting based on the capability
according to an embodiment to detect the leading edge, trailing
edge, midpoint and pattern of the flux distribution across the
vertical plume cross-section to predict the vertical dimensionless
angular value.
[0161] According to another aspect, quantifying the emission source
without assuming a circular cross-section then becomes calculating
the plume area using the formula for an ellipse
(area=pi*height/2*width/2). Previously the plume width was
calculated to be 66.3 m. The plume height is calculated using the
dimensionless angular height of 27.8 degrees projected at 105.4 m
(distance from observation to source).
Plume height=sin(27.8)*105.4=49.1 m.
It follows that the plume area is:
Plume area=pi*66.3/2*49.1/2=2559 m.sup.2
It follows that the emission rate is then calculated as:
Emission rate=2559 m2*0.339 103 m3/yr/m2=868 103 m3/yr
The emission rate calculated here is lower than the one calculated
assuming a circular plume (1171 103 m3/yr) and lower than that
measured in the field with a bag a stop watch (a one point in time
measure). The accuracy of these readings is difficult to determine
at this time however, this technique of measuring the plume height
has the advantage of not assuming plume shape which may be a big
source of error. This technique also provides the added capability
of determining the concentration profile across the plume height
which gives complete flexibility to quantifying plumes of any shape
and concentration profile.
[0162] It will be appreciated that an elliptical shape was assumed
for the plume because it was coming from a point source. To
quantify area sources, a rectangular shaped plume can be used and
applying the height and width leads to a better estimate of the
emission rate. With these different shaped plumes the average flux
per unit cross-sectional footprint area will only be the maximum
(as in FIG. 11) if the concentration profile is symmetrical to the
centroid of the plume, if it is not then a weighted average is
required reflecting the concentration profile.
[0163] Good quantification is also possible if the entire width of
the plume shifts over a stationary sampling inlet 130 (or if a
mobile sample inlet 130/equipment package 110 crosses the entire
width of the plume). The profile of the flux/area versus wind speed
plot (FIG. 11) may provide a good indication if the entire plume
width/extent. If the plot in FIG. 11 shows a distribution that
rises and plateaus for a while then decreases again then this may
be an indication that the plume shifted its entire width over the
sample inlet location. If the flux/area levels do not reach a
plateau and then come down (i.e. just rise continually) then this
may be an indication only a part of the plume shifted over the
sample inlet location. Quantification is possible with only a
partially intercepted plume but it may require assumptions of plume
shape and concentration profile.
[0164] An estimate of the emission rate may be obtained as seen
from each sample inlet location. In accordance with one
implementation according to an embodiment of the invention, the
largest emission rate quantified form all sample inlet locations
may be considered as the most accurate assuming that the plume was
intercepted best from that sample inlet location. In accordance
with another implementation according to an embodiment of the
invention, the average emission rate is considered the best
estimate.
[0165] It may happen, depending on the height of a contaminant
plume and the height of a sample inlet 130 that buoyancy would
prevent collection of concentration measurements across and beyond
the entire plume cross-sectional footprint, to some extent this may
be affected by the limited wind speed conditions encountered during
the time the concentration measurements were gathered. Whether the
entire plume footprint was sampled at a sampling inlet, is
determined from the flux surfaces FIG. 9 concurrently with the
fitting (Gaussian or lambda) of the flux surface with respect to
both wind speed and direction described herein above. The degree R
of fit may be employed as a basis for asserting the degree of plume
interception. Otherwise, the degree R of fit may be employed to
assert whether the sample inlet is below or above the plume.
[0166] The assumption that the cross-section of the plume is
circular may not always be valid. For example, a non-point source
(i.e. source having a shaped opening like a mushroom cap on a tank
vent or a large area source like a city on tailings pond) may not
have a circular plume cross-section however this assumption may
improve with increasing separation distance. A ground level
emission source may have a plume that is not circular in
cross-section due to the ground effects on the wind distribution
(wind decreases closer to the ground). This may distort the lower
part of the plume and distort the overall plume shape.
[0167] In accordance with an embodiment of the invention, the
location of a contaminant source is determined by intersecting
vectors passing through the sample inlet locations in the
corresponding prevailing directions, computing emission rates for
candidate contaminant emission sources located at the intersection
and asserting the existence of an contaminant emission based on a
substantial agreement between candidate contaminant emission source
emission rates. For this purpose, if more than two sample inlet
locations are employed, then the substantial agreement is asserted
between a group of two candidate contaminant emission sources. For
a larger number of sample inlet locations, the substantial
agreement is asserted for a subgroup of candidate contamination
emission sources defined for example via a threshold number of
candidate contaminant emission sources.
[0168] Multiple plumes (with all the characteristics plumes shape,
concentration profile, variation over time, trajectory both
horizontal and vertical) observed from multiple positions are
projected out along the trajectory and grouped to determine
agreement. All or some of the possible combination of plumes can be
checked for predicted source agreement. This can be a large number
of combinations, for example if there are eight observation
positions that each observe 15 plumes the number of combinations of
two or more is 8 choose 15 which is a very large number of
calculation that will increase exponentially with increased number
of observation positions. The number of plume combinations can
become unmanageable if too many observation points are used with
too many plumes observed. The number of combinations can be reduced
by recognizing not all combinations are valid (i.e. some plume
trajectories do not converge so they don't need to be considered)
as well there may be the chance to do batches of plumes based on
predominance or shape or variability. Each combination of plumes is
scored for agreement in the source characteristics considered.
[0169] For example, the estimate of horizontal source position can
be predicted by averaging the northings and eastings of all the
instances of two trajectories crossing and this location can be
scored for agreement by summing or summing squares of the
perpendicular distance between this estimated position and all the
trajectories in the group.
[0170] Similarly, a score of the agreement in elevation estimates
can be obtained.
[0171] Based on the estimated position, the size of the source can
be estimated by averaging the size calculated for each observed
plume in the group. A score can be calculated relating the
agreement in the size estimates from each observation position.
[0172] A score of the size agreement can be obtained by summing the
difference or the square of the difference of each estimate size
from the average.
[0173] The agreement in source variability can be scored to see if
there is agreement that the source is intermittent (percent of time
it is present from each observation position) or the variability in
a constant leak. A score of the agreement in plume concentration
profile can also be obtained.
[0174] By calculating estimated sources from all the combinations
of two or more vectors projected out from the observation position
and scoring them for agreement in the characteristic categories and
then ranking scores (this ranking may be weighted toward one
characteristic or another) there will be confidence in asserting
the presents of sources at the highest ranked candidate sources.
FIG. 19 shows a map generated in this way and based on eight
observation positions, denoted by diamond shaped references
indicated by 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908. As
shown in FIG. 19, there are groupings of a small number of highly
ranked sources (indicated by references 1910, 1912, 1913, 1914,
1915, 1916, 1917, 1918 and 1919) that is likely one source. An
estimate of the true source location can be made with help of
knowing if there are potentially emitting components in the area in
the mapped area, for example as represented by the circles
referenced by 1920, 1922, 1924, 1926 in FIG. 19.
[0175] Once predominant sources are located with a high degree of
confidence then the plumes from these sources can be removed at all
the observation points and the analysis run again (i.e. in an
iterative way). In this way the dominating effect of the largest
sources and the associated plumes can be reduced and smaller more
subtle sources and characteristics are better characterized.
Mapping Using Intersections
[0176] If multiple contaminant emission sources are present, fact
which is ascertained by the number of predominant flux peaks in the
flux plots FIG. 9, then unique combination groups of prevailing
vectors are defined to include a prevailing vector from each
sampling inlets 130, and sub-groups thereof corresponding to
individual peaked flux distributions at each sample inlet location
are used to ascertain the existence of a source at the locus of
intersections of the sub-group of vectors.
Mapping Using Standard Arc Increments
[0177] Another way potential source locations may be mapped by
includes calculating an emission rate for each location about the
locus of sample inlets 130 using the flux values calculated from
the vector plots above. The area is broken down into small parcels.
Systematically one traverses through the parcels and at each parcel
the following is done: [0178] the direction and distance from the
parcel to each sample inlet location is determined using
trigonometry; [0179] using the vector plot of each sample inlet
location, the flux/area values associated with the direction from
the remote sampling location to the parcel of land is determined.
[0180] an incremental source size is calculated which is the size
of a source it would take if this parcel of land contained the
emission source that was causing the flux/area values measured and
represented in the vector plots for a small standard segment (i.e.
one degree) of such a plume. This is done by calculating a plume
area associated with this standard segment (equal to sin(standard
segment)*distance to remote monitor*assumed plume height) and
multiplying by the flux/area value from the vector plot. This is
done for each sample inlet location and will result in an
incremental source size corresponding to each remote monitoring
site. [0181] the incremental source size estimates from each sample
inlet location is used to ascertain whether the subject parcel of
land is the location of an emission source. This is done by
analyzing the distribution of the estimates, if all the incremental
source size estimates are tightly grouped around a common value
(i.e. small standard deviation) then there would be a high
confidence that this is the location of an emission source. The
estimate of the size is the average of the incremental source size
estimates. Degrees of confidence in weather this is a leak location
is determined by how many of the incremental size estimates from
the possible total number of sample inlet locations are in
agreement. [0182] Accordingly an estimate of the confidence weather
it is a leak location and the incremental size of the leak may be
obtained. On a map the parcels identified as likely a leak location
may be clustered together around an actual leak location. An
estimate of the total size of the leak is obtained by summing all
the incremental source sizes in the clustered parcels. The estimate
of the actual location is the centroid of the clustered parcels.
[0183] Each parcel of land can be assigned an estimated leak size
and a confidence. The mapping program draws the map of leak
locations by both filtering out leaks below a certain size and
confidence level.
[0184] On a plot of land far away from the source the estimates
would have a small standard deviation also, close to zero. For such
cases only the direction to a contaminant source is
ascertained.
Mapping Using Plume Width Increments
[0185] The vector plots in FIG. 10 are employed to predict the size
of a plume from an emitting source by using the width (azimuthal
width) in degrees across the peak in the vector diagram. These
plume widths in degrees can be combined with the distance from the
remote sample inlet to the parcel of land to establish the physical
width of the plume at the monitor site
Plume width=(sin(width in degrees)*distance).
Plume height=(sin(height in degrees)*distance) (if height and width
not assumed the same).
[0186] Assuming the height and width of the plume are the same (the
circular plume cross-sectional footprint assumption), the area of
the plume equals pi*(width/2).sup.2 and the average flux across the
area determined and combined to provide an emission rate. This will
be an estimate of the emission of the source that caused the plume
identified. Multiple estimates of an emission rate of a contaminant
source enable an accurate and detailed mapping of candidate
emission sources.
[0187] According to an embodiment, the mapping takes information
from the dimensionless angular plume profiles (derived from the
Flux versus wind velocity plots) of the observation positions that
are relevant to the area being mapped. A plume or a subset of a
plume can be projected out along the trajectory for mapping
purposes. Individual readings with zero angular width can be
projected back along the trajectory as well but a dispersion rate
or angular width would have to be assumed in order to adjust the
information depending on the distance projected.
[0188] The following are possible reasons why the entire plume may
not be projected: [0189] when plume boundaries are not identified
because there is not enough data [0190] when plume boundaries are
not defined because there are many plumes blending together [0191]
when plume boundaries are not defined because there is a large area
source causing the plume. Mapping can be done by projecting out
along the trajectories of individual readings or groups of readings
(grouped based on observation position and meteorological
conditions) and correcting the plume information based on the
distance projected. Projections from single or multiple observation
positions will converge in agreement at the location or area of the
true source. These projections can provide the bases to assert the
mapped locations, profiles and characteristics of point sources,
area sources, or multiple point sources.
[0192] When the dimensionless profiles show the absence of plumes
this can also be used as valuable information for mapping.
Projecting out the absence of plumes can be an indication that the
mapped area in question is free of sources. It must be kept in mind
that there is a chance a source exists but the observation point
missed the plume because it was at the wrong elevation or the
source is intermittent and was not emitting when the meteorological
conditions were present that would move the plume to the
observation point.
Calculating Overall Emissions Rate
[0193] The overall emission rate is calculated by adding the sizes
of the important individual leaks to an estimate of the smaller
leaks that blend into the background. Quantifying the larger leaks
was discussed earlier. The plumes from numerous small leaks at
facilities can blend together and may not be individually
identifiable on the vector plot in FIG. 10. The emissions rate of
these emission sources is estimated by: (average flux/area
value)*sin(degrees width of the vector plot)*assumed distance to
the cluster*assumed plume height. The assumed plume height is a
function of the distance to the emission sources. The assumed
distance to the cluster of sources is estimated from a map.
Alternatively in some situations the location of the remote
sampling inlets allows for a rough triangulation to estimate the
rough location of the cluster of emission sources which can be to
predict the distance between the cluster and the remote sample
inlet.
Characterizing the Variability in an Individual Sources Emission
Rate (Stability, Frequency, Magnitude of Swings)
[0194] The characteristics of an individual emission source can be
characterized over time by analyzing the concentration measure that
was taken from within the emission plume boundary. The
concentration measurements taken within the plume boundary of a
particular source are identified by ranges of wind direction and
wind speed at remote sampling locations. The ranges of wind
direction associated with a leak are determined in the vector plot
of FIG. 10 by the boundaries of the peaks (246 to 285 degrees). The
wind conditions associated with a particular leak are determined by
the areas of the flux/area plotted vs. wind speed in FIG. 11 and
are the wind speeds associated with level well above zero (7 to 29
kph). A better characterization of the variability may be obtained
if just the plateau part of the curve is included in the analysis
(15 to 25 kph). One can select the concentration measures from the
registry of a remote sampling location for the wind speed and
direction condition associated with a particular leak and analyze
then over time to check for variation in the leakage rate. These
measures are stored in the bins with the time stamp of when they
were taken. The concentration measure may be multiplied by the wind
speed of the compartment were they were stored to obtain a
flux/area valve that is analyzed over time as well and may give a
more sensitive estimate of the leak characteristics. FIG. 18a shows
a scatter plot of the daily average of concentration measure of
H.sub.2S versus time corresponding to a remote sampling location.
There is not a data point for all the days as the wind did not
always blow in the appropriate direction for the remote sample
point to pick up the leak. The data has a lot of scatter and shows
variability over time with the emission rate finishing much below
the average. The data corresponds to an actual leak that was
repaired around April 20.
[0195] The average of the concentration in these plots is
associated with leak quantification done on this leak, the amount
that running average varies away from the overall average is
considered indication of variability of the leak over time. This
variability can be calculated as a percentage ((running
average-average)/average) and plotted. Each remote monitoring site
can produce a similar plot for the same leak and compared to
provide a powerful tool to examine the variability of the leaks
overtime. As the multiple remote monitoring sites agree on the
trends in the variability, strong conclusions can be drawn on leak
variability.
[0196] The variation can be plotted in units of leakage rate as
shown in FIG. 18b which shows the variability of an emission source
(venting condensate tank) over time and FIG. 18c which shows the
variability in a large emission source over time. The variations
are tracked according to a time scale. By using an exaggerated or
distorted time scale the times the plume is not intersected by one
of the sample inlets are removed. FIG. 18d is the same data plotted
in FIG. 18c except that the time scale is not distorted. FIG. 18d
shows that there are periods of time when none of the eight
observation positions used in collecting data intercepted the
plume.
[0197] This technique characterizes intermittent leaks or venting.
These measurements of concentration associated with a particular
leak are analyzed to look for repeatable patterns that occur on a
daily, weekly, monthly or annual cycle. If this was to be done on a
daily cycle then the measures would be converted to percentage
difference from the average. A running average of the percentage
difference form the average is plotted and inspected for variations
within the day. Similar plots from each remote sampling site are
prepared and compared for consistency in predicting daily trends or
anomalies. A similar approach may be taken for any repeated time
cycle (i.e. weekly, monthly, annually). Monitoring emission
variability is possible at any time cycle and could for example
compare the emission rates to different crew shifts.
Quasi Real-Time Surveillance
[0198] Near real time surveillance for the occurrence of new
significant leaks is possible by analyzing new data (or blocks of
new data) soon after collection, and comparing the concentration
measurements to those already in the bins of a remote sampling
inlet. A process checks if the new data is significantly (this
significant level is a parameter that can be set) different than
the previously acquired data. If the differences exceed a certain
limit, then a flag is raised to warn that an unexpected leak has
changed, or perhaps a new leak has appeared. Operators may have the
option to waive the warning and add the data to the bins or to keep
the measurements separate while more measurements are collected,
when the new measurements could be discarded (if the anomaly in the
facility was identified and fixed) or added to the bins if it is a
change in operation that will continue. The new data is compared to
any historical time period by setting a parameter. This time period
is implemented as a rolling window that moves forward.
Real Time Monitoring
[0199] According to this embodiment, surveillance is provided to
determine a changing emission source in real time or near real
time. According to this embodiment, the data streaming from a
detector is tracked in near real time and compared to the readings
of what is to be expected and taking specific action if unexpected
readings are received. What is expected can be based on the pattern
of emissions at an observation location developed from historical
measurements or assumed, for example, one could assume no emission
sources or expected patterns based on known emission sources. When
readings are not what we expected, then we one or more of the
following actions can be taken: [0200] 1. do nothing different
[0201] 2. check if the reading is real or an artifact by [0202] a.
checking equipment function [0203] b. is there repeated evidence of
a deviation from this observation point [0204] c. is there
confirmation of the deviation from other observation positions
[0205] d. check if there is any record of similar deviation logged
with similar circumstances [0206] 3. log the deviation for future
reference [0207] 4. sound an alarm [0208] 5. refine the mapping of
the new source by reanalyzing the data with more accurate
assumptions (i.e. distance to source) [0209] 6. changing where
samples are being taken from to have the best chance at
intercepting the plume of the new or changing source. This is
refocusing the data collection to "chase" the new or changing
emission source [0210] 7. subtracting the variant signal from the
background at all sample inlets and mapping the differences from
the background Where an area has been under surveillance for some
time there may be a substantial data history that will enable
statistical power in asserting whether a reading is out of the
ordinary or not. If there is not historical measures to compare
readings to then the thresholds to take different action at can be
assumed.
[0211] This near real time surveillance may be applied to mobile
detectors. If an area has been traversed by mobile detectors before
then there may be a history able to compare new readings to that
are geographically and meteorologically specific. If a lot of data
has been collected over certain paths or an area, then the
statistical power to predict new or changing emission patterns
increases. If it is the first time an area has been traversed by
mobile monitors then one may not be able to be as sensitive to new
or changing emission sources and one will have to rely on
thresholds for asserting the presents of sources based on assumed
values derived from experience.
Focused Monitoring
[0212] Focused monitoring refers to the technique of changing which
remote sampling sites are analyzed based on an analysis of wind
conditions. Under normal conditions the valves that control which
remote sample location gets analyzed are on a predetermined cycle
that steps through every valve in sequence and hold for a
predetermined length of time at each valve (i.e. three minutes at
each valve position). Under focused monitoring the valve sequencing
is adjusted to reflect expected or measured wind conditions. This
technique is useful if information on fugitive emissions is desired
from a particular location in a facility. If the predominant wind
is from the west then the valve sequencing is predetermined to
pause for longer time periods at the eastern remote sampling
locations if the focus of the analysis is on the facility. The
valve sequencing can be adjusted on the fly as well to accommodate
changes in the wind direction by adopting predetermined sequencing
patterns for different conditions of wind direction and speed.
Lastly the valve sequence can be adjusted to focus the surveillance
at new areas that arise from the results that emerge from the quasi
real time surveillance. If the quasi real time surveillance
identifies something different occurring in the air concentration
measurements and the triangulation algorithms establish the area of
the anomalous emissions then the focused monitoring algorithms
adjust the valving sequences based on the current wind direction to
provide surveillance to the emerging area of concern, for example,
chasing down a new leak).
[0213] According to another embodiment, the data collection or
observation position locations can be adjusted to "chase" the
emitting source. In general it involves moving the observation
point to better intercept the plumes from sources.
[0214] According to another aspect, the raw data can be re-analyzed
with new assumptions to refocus the analysis and get a clearer more
accurate picture in a adjusted location. Initially analysis
requires assuming a distance to the source once a good idea of
source locations are obtained, the raw data is re-analyzed with
better assumptions of the distances from each observation position
to the sources and more accurate characterization of the sources
can be achieved.
Longer Time Needed to Characterize Intermittent Leaks
[0215] Continuous leaks will provide a constant signal that may
allow for the characterization of sources when the wind blows in
the appropriate directions. The length of time needed to adequately
characterize an area for emission sources is dependent on the
frequency of the wind blowing from the appropriate direction and
the frequency of the cycling of the remote sampling array. Focused
monitoring can shorten the time needed by adjusting the frequency
of the remote sampling array to match the frequency of wind blowing
the right way. With intermittent sources there is the frequency of
the emission source to overlay on top of the frequency of the
remote sampling array and the frequency of the wind blowing the
right way.
[0216] In summary, to obtain concentration measurements on a
contaminant source the wind must be blowing in the right direction,
the remote sampling inlet must be active, and the source has to be
emitting all at the same time. If these three requirements are
satisfied only infrequently, then it can take a long time to gather
enough data to characterize the emission source. Adjustments can be
made to try and decrease the time needed by moving the location of
the remote sampling inlet or adjusting the frequency of the remote
sampling array through focused monitoring. In general intermittent
sources that emit infrequently will take longer to
characterize.
Mapping and Stating the Confidence of the Predictions
[0217] Early (timely) contaminant source predictions may be
valuable even if they have higher margins of error than later
confident contaminant source predictions. The confidence in
predictions made may be included in output to help with the
interpretation of the results. This allows for the release of early
results with warnings of margins of error that will be different in
the different areas of the map depending on the wind directions
that have occurred. Predictions based on numerous measurements may
be very accurate while prediction based on few measurements will
have higher margins of error but still may be valuable if delivered
in a timely manner.
Predicting Elevations of Emission Sources
[0218] The pattern of the flux/area values in FIG. 11 will be
different depending on the elevation of the remote sample inlet,
the elevation of the source, and the rising nature of the plume due
to buoyancy or momentum (up or down). The following describes how
the elevation can be predicted (for example, see FIG. 16): [0219]
the vertical velocity (Vy) of the plume is estimated as:
[0219] Vy=c/(a*(1/Vx1-1/Vx2)) [0220] where Vx1 and Vx2 are
different wind speeds when the plume center intercepts the remote
sample inlets r1 and r2. [0221] then the height of a fugitive
source (b) is determined
[0221] b=r2h-(Vy*a/Vx2)*=r1h-(Vy*a/Vx1) [0222] where r2h and r1h
are the height of the remote sampling inlets and a1 and a2 is the
distances from the emission source to the remote sample inlets (in
this example in the remote sample inlets are at the same location,
a1=a2).
[0223] A single monitor is able to use the two different wind
speeds that the monitor intercepts the top and bottom edge of the
plume to estimate the source height by knowing the physical size of
the plume (height=width from quantifying section=(sin(39)*a). This
method may also be used to estimate plume dimension if Vy is known.
According to another embodiment, non-linear vertical trajectories
of the plumes due to weather inversions, plume buoyancy, land
topography, or obstacles like buildings, and the like, are
considered.
[0224] FIG. 17 shows two flux/area versus wind speed plots taken
from two remote sampling inlets at the same location but at
different elevations (13.7 m and 18.3 m). The following is a sample
calculation of the elevation of the emitting source:
Vy=c/(a*(1/Vx1-1/Vx2))
c=(18.3-13.7)=4.6 m
Vx2=21 kph=5.83 m/s
Vx1=18 kph=5.0 m/s
a=105.4 m (from previous)
Vy=1.53 m/s
b=r2h-(Vy*a/Vx2)
r2h=13.7 m
b=-14.0 m (the remote sample location was at higher evaluation)
[0225] The apparatus and methods presented herein may be employed
to locate and quantify any source of airborne emissions as long as
the emission contains a compound or particulate which can be
detected with an analyzer at levels distinguishable above
background concentrations thereof.
[0226] Reference is next made to FIGS. 20 to 23 which show in
flowchart form processes or methods according to embodiments of the
present invention.
[0227] Reference is made to FIG. 20 which provides an overview
process flow of processes according to an embodiment of the
invention. The processes as depicted in FIG. 20 comprise a
monitoring/tracking process 2010, a mapping process 2020, and a
data acquisition or surveillance process 2030.
[0228] The data acquisition/surveillance process 2030 is concerned
with the collection of contaminant concentration and meteorological
related data according to an embodiment. The collected data is
stored in a database or a datastore/archive indicated generally by
reference 2040 in FIGS. 20 and 21. According to an embodiment, the
process 2030 can collect or manipulate data based on input from the
mapping process 2020 and/or the monitoring/tracking process 2010,
as depicted in FIG. 20. According to an embodiment, the
monitoring/tracking process 2010 checks data from the data
acquisition process 2030 against baseline data to determine changes
in emission sources and/or emission levels in the area being
monitored. According to an embodiment, the monitoring/tracking
process 2010 is implemented to modify or refocus the operation of
the data acquisition process 2030 and/or the mapping process 2020
in response to one or more monitored events. According to an
embodiment, the mapping process 2020 generates one or more maps
that identify the location(s) of emission sources based on data
collected by the data acquisition process 2030. The mapping process
2020 may also utilize data retrieved from the datastore 2040.
According to another aspect, the mapping process 2020 is
implemented to refocus or alter the map based on input from the
system user, the monitoring process 2010 and/or previously
generated maps. According to another aspect, the map(s) generated
according to the mapping process 2020 are utilized to target
surveillance and/or monitoring/tracking.
[0229] Reference is next made to FIG. 21, which shows an embodiment
of the data acquisition or surveillance process 2030 in more
detail. According to one aspect, the data acquisition process 2030
is implemented with "where" and "when" parameters for performing
surveillance, i.e. data acquisition. The "where" parameter defines
the area or space under surveillance and has virtually no limits,
for example, data can be acquired for a large area, e.g. a province
or country, or for a small area, e.g. an industrial facility,
building or compound, or both with a maps showing larger areas with
focus on small or sub areas. The "when" parameter defines the time
frame of the surveillance or data acquisition, and can go back in
time based on previously captured or archived air quantity data and
meteorological data is available. According to another aspect, the
"when" parameter can be defined for near or real time data
acquisition.
[0230] According to an embodiment, the data acquisition process
2030 relies on user input 2110 to define the parameters for time
and space as indicated by reference 2112 in FIG. 21. According to
another aspect, the process allows the user to interact with the
surveillance or data acquisition by changing the original
objectives, for example, to better characterize an identified or
potential emission source, as indicated by 2120. For example, the
user can identify an area and time to focus the surveillance or
indicate an area for special monitoring. According to another
aspect, the process triggers the mapping process 2020 (FIG. 20 and
FIG. 22) to update the map. As also shown in FIG. 21, the data
acquisition process 2030 includes the capability to gather
information or data on land use, topography, buildings,
obstructions, known sources, potential sources, as indicated by
reference 2122. For example, available information is gathered on
land use and topography or obstructions that may affect the
movement of the air. Knowledge of these obstructions and there
geometry can be used when projecting the plume back along its
trajectory to make adjustments for the obstructions. Knowledge of
known sources and potential sources can be used in the mapping
algorithms to better estimated source characteristics.
[0231] According to another aspect, the data acquisition process
2030 is configured to collect relevant data existing for example in
historical archives, as indicated by 2130. The historical or
archived data can include contaminant concentrations (air quality)
and meteorological conditions (i.e. wind velocity) relevant to the
space and time in question from any available source. The data can
also include the observation positions of the readings that
generated the data (for example, readings could be from mobile
monitors). This data is put into the datastore 2040 and is made
available for the processes as described above. According to
another aspect, the utilization of historical datasets in
accordance with the methodology according to the present invention
provides the capability of mapping and characterizing emission
sources back through time based on data from air monitoring
networks that have been in operation.
[0232] As shown in FIG. 21, the data acquisition process 2030
includes a module for the collection new data, e.g. locations,
frequency, stationary or mobile, as indicated by reference 2140.
According to an embodiment, the process module 2140 determines the
strategy for collecting new data based on the air monitoring
equipment available and the space/time parameters set for
surveillance. For example, the monitoring equipment may be
installed in permanent location(s), part of a cycled inlet array,
stationary but movable, and mobile monitoring. Refocusing of the
surveillance on smaller locations or other locations based on user
initiated changes or the emergence of new or changing sources can
results in changes to the deployment of the monitoring equipment or
changes to the sample inlet locations to better track the new areas
or the new sources.
[0233] As shown in FIG. 21, the data acquisition process 2030
includes a module 2150 for taking measurements of emissions, e.g.
contaminant concentration measurements. The module 2150 utilizes
detectors for example as described above, and in general, any
detector of the target compound can be used (including open path
detectors which would be a line or plane of observation rather than
a point of observation for traditional detectors). According to an
embodiment, the detectors are connected to a manifold and are
cycled through multiple remote sample inlets that would enable
multiple observation positions from one detector. The monitoring
equipment may be installed in permanent locations, part of a cycled
inlet array, stationary but movable, and mobile monitoring.
[0234] As shown in FIG. 21, the data acquisition process 2030
includes a module 2160 for gathering or making meteorological
measurements. As described, wind velocity is used to determine
emission sources according to an embodiment. In addition to wind
velocity, other data suitable for predicting air movement or
vertical mixing characteristics of the atmosphere (e.g. temperature
inversions) including but not limited to vertical wind speed, sun
light intensity, temperature, and humidity can be gathered or
measured. According to another aspect, any relevant public
meteorological data can be collected and/or with the deployment of
other meteorological equipment used to fill in any gaps. The
meteorological conditions for the time and space the plumes are
being traced through will be based on interpolations and
extrapolations of the meteorological data available taking into
account the effects of topography or obstructions on air
movement.
[0235] As shown in FIG. 21, data captured or collected by the data
acquisition process is stored in the datastore 2040. According to
an embodiment the datastore 2040 holds the relevant time stamped
information and makes it available to the mapping process 2010 and
the quasi real-time surveillance algorithm. Determining relevant
information is based on the objectives and what is available.
[0236] Reference is next made to FIG. 22, which shows in more
detail an embodiment of the mapping process 2010. As shown, the
mapping process 2010 includes a process module 2210 for generated
or updating a more detailed or focused map. According to an
embodiment, the process module 2210 is triggered or invoke by a
preset time-out or interval, a user input or a trigger from a
real-time monitoring operation. In response, a process is initiated
to update the emissions map. A focused map is achieved by mapping
sources with good assumptions of the distance from the source to
the observation positions. Areas of maps may have different
distance assumptions than other areas depending on the relative
positions of the observation potions and the source locations.
According to one aspect, a map can comprise a patchwork of smaller
sub areas with specific area focus. This patchwork can be achieved
by either zeroing in on the important sources through and iterative
approach or a brute force approach that breaks the area down into
smaller areas and generating a focused map for each smaller area
and then combining them into the larger overall map after. The
second approach may be better for area sources because depending on
the proximity of the observation positions, the area sources may
not converge with a zeroing in approach.
[0237] According to an embodiment, the mapping process 2010
includes a process module 2220 for determining assumptions that
support time and space focus. When the mapping refresh starts the
first step is to gather the data from the archive and determine the
applicable and current time and space focus from the surveillance
and monitoring routines.
[0238] According to an embodiment, the mapping process 2010
includes a process module 2230 for calculating dimensionless plume
profiles, for example, as described in more detail above. The
current profiles are stored for reference. These profiles are
calculated by projecting contaminant readings back along the
trajectory traveled and analyzing as described previously.
[0239] According to an embodiment, the mapping process 2010
includes a process module 2240 for providing surfaces associated
with the dimensionless plume profiles. The profiles are pasted on
to the mapping process and are available to the monitoring routine
establish the expected values to compare with new reading(s).
[0240] According to an embodiment, the mapping process 2010
includes a process module 2250 for generating a map. The process
module 2250 takes the inputs and predicts source characteristics
including location. As indicated by reference 2260 in FIG. 22, the
map comprises individual sources (size, variability, location),
area sources (location, area profile, size, variability) and can
also include information available on the sources for this area or
sub area. According to another aspect, the map shows areas or
regions that do not have any emission sources, i.e. are emission
source free. It will be appreciated that an observation position
that does see any plumes in its direction or quadrant provides an
indication that the area is emission source free. As shown in FIG.
22, the mapping process 2010 includes a process module 2270 for
comparing sources location and timing with space/time focus (maybe
source specific). According to an embodiment, the estimated source
information is compared to the assumptions used in the calculation
to see if any refocusing is required. Any adjustments needed to the
assumptions are communicated with the appropriate process or
routine. As shown in FIG. 22, the mapping process 2010 includes a
process module 2280 for updating the map or sub-areas of the map.
According to an embodiment, the updates are intended to show
characteristics of important sources, for example, individual and
overall area emissions, or the emergence of new or changing
sources. According to an embodiment, the maps of any sub-areas that
have been generated in previous runs of the mapping routine are
combined (i.e. pieced together) to form the overall map.
[0241] Reference is next made to FIG. 23, which shows in flowchart
form an embodiment of the monitoring/real-time tracking process
2010. The monitoring process 2010 includes a process module 2310
for tracking or collecting new contaminant readings, observations
and/or meteorological conditions. According to an embodiment, the
process module 2310 process grabs the new contaminant reading along
with where the reading was taken and the relevant meteorological
conditions. If there are multiple detectors deployed then there
will be multiple sets of data. The process module 2310 passes this
information to a process module 2320 which comprises a comparison
operation.
[0242] According to an embodiment, the process module 2320
determines what contaminant levels are expected at the given
observation points under the given metrological conditions.
According to one aspect, the process module 2320 determines the
expected levels based on assumed background levels and/or assumed
action thresholds and/or historical data summarized in the
dimensionless plume profiles. Next in decision block 2330, the new
readings are compared to the expected levels. The next step depends
on whether it is found to be different or not. If there is more
than one contaminant detector collecting information then this
decision will be made for each information stream. If they are not
different then nothing happens and normal conditions signaled to
the user. If the concentration levels are not in line with the
expected levels, then a process module 2330 to archive the levels
is executed. According to an embodiment, the process module 2330
gathers the readings including any relevant position and
meteorological information and sends it to a variant archive 2332
for storage and future reference. The variant archive 2332 stores
unexpected readings and relevant associated information
(meteorological and observation position). Data can be removed from
this archive if it seen as no longer relevant to the issue of new
or changing emission sources. This may be after a prescribed time
period, if the changing emissions have been resolved, or based on
the user electing to ignore the variances. This archive is past to
the next step where it is analyzed for every unexpected or group of
unexpected readings.
[0243] According to an embodiment, the process module 2330 analyzes
variant characteristics. According to an aspect, the process module
2330 checks the archive of unexpected readings looking for
consistencies that would establish proof of an important emerging
or changing emission source. If the information is inconclusive or
incomplete, the process provides direction how to collect more or
better data or improve the analysis to make better decisions. In
general this process decides what action to take by sending
indicators to the following decision steps to trigger actions:
[0244] 1. Sound Alarm Process (2340)--according to an embodiment,
an action is triggered where the users are alerted to a new or
changing emission source. This process will notify the user of an
alarm conditions and indicate best estimate of source
characteristics and location on map. This alarm could be on the
live map that is updated periodically in the mapping algorithm.
This alarm could convey the information available about the new or
changing source including size or location on the map (to the best
accuracy available at the time. This process communicates with the
mapping process 2020 (FIG. 20). [0245] 2. Map Deviations Process
(2350)--according to an embodiment, a variant archive being sent to
the mapping process 2020 (FIGS. 20 and 22) is triggered to generate
a map of the unexpected readings with the appropriate time and
space assumptions. The mapping could us the variant readings or the
difference between the variant reading and what is expected. The
second approach would track the difference from the background and
will be able to differentiate new or emerging sources over and
above any background sources that exist. This action will
communicate with the mapping process 2020 (FIGS. 20 and 22). [0246]
3. Refocus data collection process (2360)--according to an
embodiment, a trigger is generated to adjust how the new data will
be collected to better track or "chase" a new or emerging source.
It will be based on whether you want to disrupt the existing
sampling routine to gather different information that may be more
applicable to new or changing sources. The adjustment to the data
collection will be made in the surveillance process 2030 (FIGS. 20
and 21). This process may determine how to best make the adjustment
and communicate this with the surveillance routine. The adjustment
could entail adjusting the sampling positions to better intercept a
plume from a suspected emerging or changing source. The sampling
position can be adjusted by changing the active sample inlets on an
inlet array, move a mobile detector or redeploy a detector or inlet
to be in a different position. These adjustment are to support a
new time and space focus. This process communicates with the
surveillance process 2030 (FIGS. 20 and 21). [0247] 4. Refocus data
analysis with action process module (2370)--according to an
embodiment a trigger is generated to change the space and time
focus assumptions used in the mapping algorithm. The decision
whether to changing the space and time focus will be based on
whether one can improve the focus of the analysis by having a
better estimate as to the distance to the sources. This is part of
an interactive approach as initial assumptions of the distance and
time focus must be made (to meet the overall objectives) and then
adjusted as the mapping routine produces estimates of the location
and timing of emitting sources. Once more is known about sources
better assumptions of the distance and time to sources can be made.
Different areas of a map may have different distance to the source
assumptions for the different source. These assumptions will
continue to be adjusted as more data is collected and the sources
become better defined through out the surveillance period.
According to an embodiment, the process communicates with the
mapping process 2020 (FIGS. 20 and 22). [0248] 5. Censor variant
data process module (2380)--according to an embodiment a trigger is
generated to flag variant readings in the data store so that future
analysis may choose to include or exclude it from future analysis.
According to an embodiment, this decision is based on how the user
wants anomalous conditions to affect the historical data set and
future analysis. For example, excluding anomalies results in a
cleaner baseline which allows the monitoring algorithm to be more
sensitive future anomalies.
[0249] While extensive reference has been made to detecting gas
leaks at natural gas plants and the like, the invention is not
limited thereto, the apparatus and methods described herein may
also be used for: [0250] Fugitive emissions at industrial
facilities where it can, measure the facility wide emission rate
and localize and quantify the important sources [0251]
Characterizing known emissions sources at industrial facilities
(i.e. stack monitoring). [0252] Regional emissions monitoring
[0253] Characterizing odors in rural or urban areas [0254] Policing
applications like locating and charactering drug labs, grow-ops
[0255] Providing surveillance of buildings (security guards with
mobile monitors could characterize a hotel or apartment for
explosives or drugs. [0256] Characterizing emissions from a
community [0257] Regional surveillance of pipe line leaks [0258]
Local surveillance of pipeline leaks [0259] Characterizing emission
related to site remediation work [0260] Characterizing emission
from storage tanks or tank farms [0261] Characterizing emissions
from tailings ponds [0262] Characterizing sources and sinks of
compounds in the environment (e.g. Mercury, methane) [0263]
Calibrating air dispersion models [0264] Military applications
include locating unknown explosives
[0265] The previous description of the disclosed embodiments is
provided to enable any person skilled in the art to make or use the
present invention. Various modifications to those embodiments will
be readily apparent to those skilled in the art, and the generic
principles defined herein may be applied to other embodiments
without departing from the spirit or scope of the invention. Thus,
the present invention is not intended to be limited to the
embodiments shown herein, but is to be accorded the full scope
consistent with the claims, wherein reference to an element in the
singular, such as by use of the article "a" or "an" is not intended
to mean "one and only one" unless specifically so stated, but
rather "one or more". All structural and functional equivalents to
the elements of the various embodiments described throughout the
disclosure that are known or later come to be known to those of
ordinary skill in the art are intended to be encompassed by the
elements of the claims. Moreover, nothing disclosed herein is
intended to be dedicated to the public regardless of whether such
disclosure is explicitly recited in the claims.
[0266] The embodiments presented herein are exemplary only, and
persons skilled in the art would appreciate that variations to the
embodiments may be made without departing from the spirit of the
invention. The scope of the invention is solely defined by the
appended claims.
* * * * *
References